tao-train-dino

To train a TAO DINO model for 2D object detection, provide your training and validation dataset URIs along with the number of classes. The Skill automates the training process using your specified backbone architecture and configuration.

Running inference on a TAO DINO object detection model requires a trained DINO model and the TAO SDK Python environment. The Skill processes new images or video streams to detect and visualize objects.

Yes, you can evaluate TAO DINO model performance by providing a custom evaluation dataset. The Skill calculates evaluation metrics and visualizes the detection results on your validation data.

Yes, the TAO DINO training workflow supports various backbone architectures. You can configure these architectures within your training setup to optimize 2D object detection performance.

Automating DINO model training requires the dino, pandas, and opencv-python Python dependencies. You also need the TAO SDK installed to execute the training, evaluation, and inference processes.

The best way to deploy a trained TAO DINO 2D object detection model is to run inference on new images or video streams. You can then deploy the validated model to a server for real-time object detection.